The present invention relates to a method and a system for searching for a causal relation of trouble or a fault of a product on the basis of information concerning the fault of the product to thereby display the cause of the fault together with measures for coping with the fault, as well as a system and a method for collecting and storing information concerning the quality of the product and for searching and analyzing the information as stored.
In the fault finding diagnosis of a machine or apparatus known heretofore, a tree representing the causal relations of the faults is displayed on an image screen, wherein an event of concern is moved in the direction toward the cause of the fault through interactive diagnosis procedure, as is disclosed, for example, in JP-A-2-161567. In the case of the invention described in this publication, however, no consideration is given to means for obtaining the information from the machine or apparatus subjected to the diagnosis or means for transmitting the information to a location or system for carrying out the diagnosis. Further, there is found suggestion concerning the no probability concept representing validity of the causal relations which provides one of the basis for the decisions made in the course of tree searching. Moreover, no consideration is given to means for updating the probabilities and configuration of tree. For these reasons, this prior art technique is lacking in practicality for performing the fault diagnosis of the machines or apparatuses.
With regard to the collection and analysis of the information concerning the quality of products used by the customers, there is proposed a method of acquiring the fault information of the products by making use of bar codes, as disclosed, for example, in JP-A-63-40962. However, neither teaching nor suggestion is found in this publication concerning the data storage/manage method and the search/analysis method. Further, as the fault information, there is available only "content of the fault". Thus, this prior art method is also lacking in practicality.
In the case of the prior art techniques mentioned above, it is first noted that no consideration is made concerning the accuracy of the data which provides the basis for the diagnosis, thus giving rise to a problem with respect to the accuracy of the diagnosis performed by the system. Secondly, structurization of the data providing the basis for the diagnosis or inputting of the causal relations of the faults, to say in another way, cannot be realized unless an AI tree is effectively usable, presenting a problem with respect to the operability thereof.
Furthermore, in conjunction with the method of analyzing the quality information, it is first noted that consideration is paid to neither the items of quality data to be collected nor the means for collecting the quality data in reality, giving rise to a problem concerning the practicality of the quality control or management of the system. Secondly, neither the method of storing and managing the quality data nor the search method is taken into consideration, presenting a problem with respect to the cost performance of the system as well as the operability inclusive of the expansion-susceptibility of the system. Thirdly, the function or capability of the system is confined to the display of the faults of products actually taking place in the field or locales and the states of quality deficiency. Thus, the system play a role as a tool for pursuing the causes or factors in which the faults and the deficiencies originate. Besides, because the functions of a large scale computer are utilized directly by the users, no consideration can be paid to any fine condition setting for the search and the analysis, which in turn presents problems for the users with respect to the dynamics and the flexibility of the editing function and the analysis function.